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Diagnosis of the condition of the cutting tool is a prerequisite for the automation of manufacturing, which is aimed at ensuring the proper quality of the manufacture of the articles. It is particularly important in the aviation industry, where expensive objects are often made. A high wear blade may cause damage to the workpiece, which results in premature tool replacement. This results in an increase in the costs of machine downtime during rebuilding and purchase of more tools. In many centers around the world, this has inspired the development of a blade diagnostic system. The most widely used solutions for blade wear are the most commonly used [1, 2], for example, based on signals from force sensors, vibrations, acoustic emissions and power. An example of this is an automatic blade diagnostic system developed at the Warsaw University of Technology [3, 4], Automation and Machining Department. Another approach to the intermediate assessment of the blade state is the visual evaluation of chips [5]. These methods of assessing the state of the blade use indirect diagnostics, and thus require the connection of a certain measured quantity with the degree of wear of the blade. An alternative approach would be to use direct indicators. These include geo-metric dimensions describing tool wear. One method uses tool probes that measure mechanical wear [6] by direct contact with the tool, measuring tool tip offset. The most accurate wear measurements can be carried out with workshop microscopes, but it requires a machine tool image, which consumes a lot of time and is unacceptable in industrial practice. A separate, growing group of diagnostic techniques is the use of automated vision methods [7]. Direct observation of the tool at intervals is a promising way to obtain geometric parameters that can be used as accurate blade wear indicators. The basic criterion for solving problems in video methods is proper selection of equipment for the projected solution. It is important to choose the camera and the lens, but the key is the lighting. A well-made photo simplifies further digital processing. The simplest approach to detecting a worn tool area is to light the blade and look for reflexes, which are usually much more noticeable on the shaved part than on an unused tool. This approach was used in [8]. More complex systems offer controlled lighting. In [9], an innovative approach with two fiber optics is used to illuminate the tool. The light sources could be adjusted smoothly, which allowed for a proper flushing of the wear zone. The concept of controlled lighting was developed by using a ring light [10] to illuminate the tool from different directions. Pictures are compared with each other in terms of moving contours. Those that do not move are considered contours of wear. This allows you to reduce the effect of reflections that shift depending on the angle of illumination. In this paper, we decided to use an illuminator to illuminate the blade from 8 independent directions. Analysis of differences between subsequent images allows for a relatively simple and independent separation of the wear area, which makes it possible to conduct diagnostics of the tool state during machining breaks.

Analysis of differences between images with lighting from different directions for detection of tool wear